To build a machine-learned interatomic potential, first of all, a set of
reference configurations with their quantum-mechanical energies, atomic
forces and, ideally, stress tensors is required. Active learning schemes
provide an extremely efficient solution for constructing such a database,
whereby automated query strategies allow the machine to judge whether new
structures should be added to the training dataset or not [1]}.
Extensive details of the algorithm as well as of the machine-learning model
can be found in Refs. [2]}, [3]}, while here only
the most relevant features are outlined. The core of our training strategy
is that the MLFF is constructed on the fly during MD simulations,
and at every MD step it is used
to judge whether a first-principles calculation should be executed and a new
structure should be added to the dataset. The decision relies on an estimation
of the errors in the predicted atomic forces for each structure on the basis
of Bayesian inference, as it will be explained later. When no new
first-principles calculations are carried out, the atomic positions and velocities
are updated using the MLFF predictions.
